Next-generations of flexible hybrid sensing platforms for edge-computing: fabrication, integration and deployment

用于边缘计算的下一代灵活混合传感平台：制造、集成和部署

• 批准号: RGPIN-2022-03083
• 负责人: Cloutier, Sylvain
• 金额: $ 4.01万
• 依托单位: École de technologie supérieure
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=753994
• 关键词: Next; generations; flexible; hybrid; sensing

The primary objective of this ambitious Discovery program is to enable high-performance fully-integrated flexible hybrid electronic (FHE) sensing platforms for edge-computing. This fundamental research into the fabrication, integration and deployment of ultra low-cost materials and devices is essential to provide new and better ubiquitous sensing and data processing platforms. This program builds on my team's unique expertise and track record in the fabrication and integration of unique optoelectronic device architectures using new functional materials, cutting-edge digital inkjet printing and emerging post-processing & assembly techniques. Leveraging this fundamental research remains absolutely essential in order to eventually see those exciting new ultra low-cost materials and devices spawn viable products. At the same time, we also wish to explore a different approach to further enhance the physical performance of our printed sensor devices using recent breakthroughs in machine learning. Indeed, very preliminary results acquired in the last few months already suggest that dramatic accuracy improvements can be possible using low-cost printed sensors together with existing machine learning techniques. In turn, we firmly believe that combining more conventional sensor designs with on-chip machine learning capabilities to run inferences locally can also provide much better datasets, while alleviating some of the latency and security concerns associated with cloud computing. As such, the outcome of this ambitious multifaceted research program will be to provide the scientific community and the Canadian industry with the new knowledge and necessary tools to meet the exponentially-growing demand for sensing devices targeting a wide range of applications including wearables & health monitoring, advanced manufacturing, IoT & smart building platforms. In turn, these new sensing platforms will help provide more complete and higher-quality datasets in order to exploit new machine learning and artificial intelligence tools to their fullest capabilities.

这一雄心勃勃的 Discovery 计划的主要目标是为边缘计算提供高性能、完全集成的灵活混合电子 (FHE) 传感平台。这种对超低成本材料和设备的制造、集成和部署的基础研究对于提供新的、更好的普适传感和数据处理平台至关重要。 该计划建立在我的团队在使用新型功能材料、尖端数字喷墨印刷以及新兴后处理和组装技术制造和集成独特光电器件架构方面的独特专业知识和跟踪记录的基础上。为了最终看到那些令人兴奋的新型超低成本材料和设备产生可行的产品，利用这项基础研究仍然绝对必要。 与此同时，我们还希望探索一种不同的方法，利用机器学习领域的最新突破，进一步增强印刷传感器设备的物理性能。事实上，过去几个月获得的非常初步的结果已经表明，使用低成本印刷传感器与现有的机器学习技术可以显着提高精度。反过来，我们坚信，将更传统的传感器设计与片上机器学习功能相结合以在本地运行推理也可以提供更好的数据集，同时减轻与云计算相关的一些延迟和安全问题。 因此，这一雄心勃勃的多方面研究计划的成果将为科学界和加拿大工业界提供新的知识和必要的工具，以满足对传感设备呈指数增长的需求，这些设备针对包括可穿戴设备和健康监测在内的广泛应用、先进制造、物联网和智能建筑平台。反过来，这些新的传感平台将有助于提供更完整、更高质量的数据集，以便充分利用新的机器学习和人工智能工具。